# Screening maps of the local Universe I -- Methodology

## TL;DR

This paper presents a new methodology for creating detailed screening maps of the local Universe to identify regions where deviations from General Relativity are suppressed, using Bayesian mass reconstruction and applying it to $f(R)$ gravity models.

## Contribution

It introduces a novel approach combining Bayesian mass reconstruction with modified gravity equations to improve predictions of screening effects in the local Universe.

## Key findings

- First qualitative screening results for $f(R)$ gravity.
- Demonstration of the methodology's potential for future observational tests.
- Framework applicable to various modified gravity models.

## Abstract

We introduce the LOCal Universe Screening Test Suite (LOCUSTS) project, an effort to create 'screening maps' in the nearby Universe to identify regions in our neighbourhood which are screened, i.e., regions where deviations from General Relativity (GR) are suppressed, in various modified gravity (MG) models. In these models, deviations from the GR force law are often stronger for smaller astrophysical objects, making them ideal test beds of gravity in the local Universe. However, the actual behaviour of the modified gravity force also depends on the environment of the objects, and to make accurate predictions one has to take the latter into account. This can be done approximately using luminous objects in the local Universe as tracers of the underlying dark matter field. Here, we propose a new approach that takes advantage of state-of-the-art Bayesian reconstruction of the mass distribution in the Universe, which allows us to solve the modified gravity equations and predict the screening effect more accurately. This is the first of a series of works, in which we present our methodology and some qualitative results of screening for a specific MG model, $f(R)$ gravity. Applications to test models using observations and extensions to other classes of models will be studied in future works.